Design of Robot Welding Seam Tracking System with Structured Light Vision

Robot welding is an important developing direction of welding automation and intelligentization, and automatic seam tracking technology is one of principal research domains. Nowadays, seam tracking system with structured light vision becomes a hot research. Structured light vision seam tracking products abroad are generally very expensive and can only be applied on special occasions. In China, the research of structured light vision seam tracking system is still just on the stage of experiments. A robot real-time seam tracking system with line structured light vision is designed. The hardware system is set up, a filtering method for line structure seam image is improved, and compared with common filtering, it has better effect and characteristic of real time. Two methods, fast template matching and fast Hough transform, to recognize the image coordinates of seam center are improved. Two new image recognition methods, structure element matching and comer detecting, are proposed. The comparison of seam image recognition shows that fast template matching and comer detecting are more precise and stable than the other two methods, and comer detecting is the best in real time. A simultaneous calibration for camera parameters and robot hand-eye is also proposed, and calculation shows that the calibration is effective and feasible. The robot seam tracking tests for linear and folded lap-joint are performed, which are based on the above four image recognition methods, and the results indicate that four image recognition methods are all applicable to real-time seam tracking, and the whole system sufficed for the requirements of real-time seam tracking. Automatic seam tracking with line structured light vision is feasible and has good versatility.

Seam Tracking and Visual Control for Robotic Arc Welding Based on Structured Light Stereovision

A real-time arc welding robot visual control system based on a local network with a multi-level hierarchy is developed in this paper. It consists of an intelligence and human-machine interface level, a motion planning level, a motion control level and a servo control level. The last three levels form a local real-time open robot controller, which realizes motion planning and motion control of a robot. A camera calibration method based on the relative movement of the end-effector connected to a robot is proposed and a method for tracking weld seam based on the structured light stereovision is provided. Combining the parameters of the cameras and laser plane, three groups of position values in Cartesian space are obtained for each feature point in a stripe projected on the weld seam. The accurate three-dimensional position of the edge points in the weld seam can be calculated from the obtained parameters with an information fusion algorithm. By calculating the weld seam parameter from position and image data, the movement parameters of the robot used for tracking can be determined. A swing welding experiment of type V groove weld is successfully conducted, the results of which show that the system has high resolution seam tracking in real-time, and works stably and efficiently.

Study of image processing for V-shape groove and robotic weld seam tracking based on laser vision

Single-stripe laser was applied to acquire V-shape groove contour information. Most of arc light and splash noise was removed and stripe laser image was kept by wavelet transform. Half-threshold algorithm was used for image segmentation and stripe laser image was gotten after refining. Weld seam center position was identified and extracted by extreme curvature corner detection method. The location of torch was detected to accord the frequency of computer program with robot program and serial communication program. The tracking experiments of sidelong, reflex and curve weld line show that the system can meet the demand of the tracking precision under normal welding conditions.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Study on the image processing of laser vision seam tracking system

Seam image processing is the basis of the realization of automatic laser vision seam tracking system, and it has become one of the important research directions. Adding windows processing, gray processing, fast median filtering, binary processing and image edge extraction are used to pretreat the seam image. In the post-processing of seam image, the feature points of the target image are succesfully detected by using center line extraction and feature points detection algorithm based on slope analysis. The whole processing time is less than 150 ms, and the real-time processing of seam image can be implemented.

Hybrid Visual Servoing Control for Robotic Arc Welding Based on Structured Light Vision

A novel hybrid visual servoing control method based on structured light vision is pro-posed for robotic arc welding with a general six degrees of freedom robot. It consists of a positioncontrol inner-loop in Cartesian space and two outer-loops. One is position-based visual control inCartesian space for moving in the direction of weld seam, i.e., weld seam tracking, another is image-based visual control in image space for adjustment to eliminate the errors in the process of tracking.A new Jacobian matrix from image space of the feature point on structured light stripe to Cartesianspace is provided for dierential movement of the end-e?ector. The control system model is simplifiedand its stability is discussed. An experiment of arc welding protected by gas CO2 for verifying iswell conducted.

Vision Sensing-Based Online Correction System for Robotic Weld Grinding

The service cycle and dynamic performance of structural parts are afected by the weld grinding accuracy and surface consistency. Because of reasons such as assembly errors and thermal deformation, the actual track of the robot does not coincide with the theoretical track when the weld is ground ofine, resulting in poor workpiece surface quality. Considering these problems, in this study, a vision sensing-based online correction system for robotic weld grinding was developed. The system mainly included three subsystems: weld feature extraction, grinding, and robot real-time control. The grinding equipment was frst set as a substation for the robot using the WorkVisual software. The input/output (I/O) ports for communication between the robot and the grinding equipment were confgured via the I/O mapping function to enable the robot to control the grinding equipment (start, stop, and speed control). Subsequently, the Ethernet KRL software package was used to write the data interaction structure to realize realtime communication between the robot and the laser vision system. To correct the measurement error caused by the bending deformation of the workpiece, we established a surface profle model of the base material in the weld area using a polynomial ftting algorithm to compensate for the measurement data. The corrected extracted weld width and height errors were reduced by 2.01% and 9.3%, respectively. Online weld seam extraction and correction experiments verifed the efectiveness of the system’s correction function, and the system could control the grinding trajectory error within 0.2 mm. The reliability of the system was verifed through actual weld grinding experiments. The roughness, Ra, could reach 0.504 µm and the average residual height was within 0.21 mm. In this study, we developed a vision sensing-based online correction system for robotic weld grinding with a good correction efect and high robustness.

基于结构光的飞机蒙皮对缝自主测量跟踪技术

针对飞机蒙皮对缝间隙和阶差的自动化测量问题,以线结构光视觉检测技术为基础,研究了蒙皮对缝自主测量跟踪方法。将传统的基于图像形态学的处理方法和光流法结合,实现了对蒙皮对缝特征的实时跟踪,从而确定间隙和阶差的测量位置。通过实测数据对手眼标定参数进行修正,提高了对缝跟踪的精度。结果表明,当跟踪速度为7 mm/s时,对缝跟踪的位置精度大于0.500 mm,对缝跟踪的姿态精度大于0.5°,跟踪系统具有较好的准确性和稳定性,能够满足飞机蒙皮对缝跟踪的要求。

煤矿井下单轨吊无人驾驶目标识别算法与轨道接缝检测方法

单轨吊作为煤矿井下辅助运输的重要设备形式之一,具有运载能力大、爬坡能力强等优点,在煤矿智能化建设背景下无人驾驶是其必然的发展方向。为确保无人驾驶单轨吊在井下巷道内的安全行驶,对轨道接缝和关键目标的可靠检测尤为重要,从提高单轨吊无人驾驶安全通过性出发,对单轨吊无人驾驶目标识别算法与轨道接缝检测方法两大主要方面展开了研究。首先,对矿井图片数据集进行了增强处理,提高了其多样性;提出了一种加入改进通道注意力机制ECA_s和EIOU回归损失函数的YOLOv5算法,并对改进后的YOLOv5算法进行试验分析,结果表明,采用改进后的YOLOv5算法,识别准确率提高了9.1%,mAP提升了3.6%。其次,建立了基于机器视觉的单轨吊轨道接缝检测方法,采用图像预处理、直方图信息统计、形态学处理等技术,根据标定系数计算轨道接缝距离,结果表明,基于机器视觉的轨道接缝检测算法处理一张接缝图像的检测误差仅为0.3 mm。最后,开展了单轨吊无人驾驶目标检测试验,结果表明,改进后的YOLOv5目标检测算法平均精度达到90.3%,具有更高的检测准确率;单轨吊轨道接缝检测算法处理接缝图像的平均检测误差为0.73 mm、最大检测误差不超过1.1 mm,具有更高的检测精度。为保障煤矿井下单轨吊无人驾驶安全通过性和可靠性,提供了务实可行的检测方案和精确可靠的检测算法。

基于传感技术的管道接头法兰环焊缝质量检测方法研究

管道接头法兰焊缝位于管道内部,无法直接检测。为此提出基于机器视觉及卷积神经网络的识别方法。通过机器视觉传感器采集影像,采用张氏标定法和最小二乘积法去除镜头畸变,获得管道接头法兰图像。基于卷积神经网络训练管道接头法兰的特征,引入非线性激活函数令输出结果非线性化,通过池化运算降低特征复杂度和避免多度拟合情况发生,凭借Softmax将特征结果转化为分类概率,实现管道接头法兰环焊缝识别。实验证明,所提方法识别性能好,精度高,迭代周期短,误差均没有超过2mm,仅用100迭代就达到了最小识别误差值。

基于机器视觉的智能建造H型钢焊接焊缝轨迹跟踪方法

为了有效提升智能建造H型钢焊接焊缝轨迹跟踪结果的准确性,提出一种基于机器视觉的智能建造H型钢焊接焊缝轨迹跟踪方法,通过机器视觉系统采集智能建造H型钢表面图像,采用非局部均值滤波和双边滤波相结合的方法进行降噪处理;对智能建造H型钢表面图像进行纹理分析,提取图像纹理特征,同时利用图像分割法确定焊缝区域;引入Shi-Tomasi算法进行角点检测,经过拟合直线求交点和逐行扫描获取焊缝坡口信息对应的拐点信息,确定焊缝中心,实现智能建造H型钢焊接焊缝轨迹跟踪。通过实际测试分析证明,所提方法可以准确跟踪焊缝轨迹,且具有较强的实时性,可以有效推进智能建造的发展。

基于改进ViT的熔池识别与焊接偏差在线检测方法

焊接偏差的精确检测是实现焊接机器人焊缝轨迹自动跟踪及智能化焊接的前提。提出了一种基于改进视觉转换器(Vision Transformer,ViT)的熔池识别与焊接偏差在线检测方法。首先,采用轻量级ViT模型Segformer作为基线模型,在其掩码分割前嵌入置换注意力(Shuffle Attention,SA)机制,以更好地捕获特征信息在空间和通道这2个维度中的依赖关系,从而提高模型的分割精度;其次,在多层感知机(Multilayer Perceptron,MLP)中加入上下文广播(Context Broadcasting,CB)模块,在保证模型低参数量的前提下提高泛化能力;最后,基于模型分割结果,提出一种焊接偏差计算方法来定量描述偏差检测精度。实验结果表明,相较于基线模型,所提出模型的平均交并比和平均像素准确率分别提高了2.67%和2.12%,且对于不同预设焊枪偏移情况均具有良好的泛化性,焊接偏差精度控制在±0.021 mm之内,为实现精密焊接焊缝跟踪提供基础。

线结构光焊缝类型识别及特征提取研究进展

在制造业中,焊接质量对于产品的安全性、可靠性和使用寿命具有重要影响,焊缝质量的检测和评估是质量控制的关键环节。基于线结构光的焊缝特征检测方法是一种非接触、高精度的检测技术,广泛应用于焊缝类型的识别和特征提取。首先详细介绍了线结构光技术的工作原理及其市场分布,强调了这一技术在焊接领域的关键作用。进一步总结了目前焊接领域的研究成果和挑战,如对环境光敏感性和复杂背景的适应问题,以及焊接材料的高反射率和吸收率对检测的干扰。最后,探讨了未来研究方向,包括机器学习技术的融合以增强算法的性能和鲁棒性,以及利用点云技术改进焊缝小轮廓区域的识别,展示了自动化焊接技术在制造业中的重要性和发展潜力。

一种基于激光结构光的焊缝跟踪视觉传感器

焊缝自动跟踪是实现焊接自动化的关键。本文在总结不同焊缝跟踪传感器研究的基础上,设计了一种基于激光结构光的视觉传感器。针对不同类型焊缝(V形、U形坡口焊缝、搭接、对接焊缝和第一道焊焊缝、多道焊焊缝等)的图像特点,开发了焊缝激光图像处理模块库,可以提取出焊缝特征点位置坐标,实现焊缝跟踪。实验证明,传感器具有较高的精度和可靠性,并对焊缝跟踪系统具有一定的普适性。

激光视觉焊缝跟踪系统图像处理

对视觉焊缝跟踪实时图像处理的方法进行了研究,首先采用图像增强来增加图像对比度,采用中值滤波去除图像噪声,并用二值化法将目标图像从背景图像中提取出来。在后处理的研究中激光视觉焊缝跟踪系统图像处理的关键技术———激光带中心线的抽取和特征点的检测提出了切实可行的方法。采用中轴变换法提取的中心线单一、连续;用斜率分析法来检测特征点方便可靠。该处理方法能准确检测焊缝特征点,处理速度快,能够满足跟踪系统的实时性要求。

视觉引导下的机器人跟踪复杂焊缝的研究

对无视觉机器人加装视觉系统进行了研究 ,给出了机器人坐标系到摄像机坐标系的变换关系 ,对改进后的机器人进行了复杂焊缝的跟踪实验 ,实验证明该改进是可行的 .

视觉传感机器人焊缝跟踪系统

利用环形激光扫描传感器,建立了一种用于焊缝定位与焊缝跟踪的弧焊机器人视觉传感系统,基于图像分割、特征提取和三维计算等技术,实现了待焊工件形状位置信息的三维数值计算,并以对接焊缝和斜坡焊缝为对象进行了焊缝定位和焊缝跟踪试验.结果表明,提出的弧焊机器人视觉系统能够实时获得被焊工件的形位信息,实现焊缝的实时定位与跟踪.

视觉传感焊缝跟踪技术的发展状况及实施方案探讨

介绍了用于焊缝跟踪的几种视觉传感方法及控制技术的原理、特点,在此基础上提出一种基于结构光焊缝激光跟踪系统的实施方案,该方案能够将三维控制转换为一维控制,控制简单,实施容易,对焊缝跟踪系统的研制有一定的参考作用。

焊管纵缝实时焊缝跟踪的机器视觉方法

介绍了一个大直径不锈钢等离子弧焊管纵缝焊缝跟踪系统。该系统利用视觉传感器将熔池与接缝拍摄在一幅图像中,检测出接缝偏差然后驱动工件运动使接缝对准焊枪。提出了一种新颖的基于机器视觉的熔池区域图像处理算法。该算法以20帧/s的速度实时处理图像并提取出三个特征量用来计算接缝偏差。试验证明这种算法是有效的,并且能够满足系统的实时响应要求。

基于激光双目视觉的接缝三维重建

设计研制了结构激光双目视觉传感器,该传感器加入结构激光,很好地解决了立体视觉中对应点的匹配问题。利用该传感器采集接缝图像,开发了图像采集处理程序,实现接缝图像处理和特征分析,识别并提取了二维平面上接缝特征点的图像坐标。采用MATLAB软件实现了三维重建标定算法,根据双目视觉原理,由接缝特征点的图像坐标计算出其三维坐标,进行了三维重建,抽取了接缝三维几何尺寸、坡口类型、角度、间隙等焊接特征信息。